Business Intelligence Exam 1 Review

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Questions and Answers

What's the most important thing about a Data Warehouse?

It becomes a single version of truth for a company.

What is a 'data warehouse'?

A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format.

What does a data warehouse do? (3 Key Terms)

The data warehouse is a collection of integrated, subject-oriented databases designed to support DSS functions.

What is relational data?

Used in data warehouses to enable collaboration between different systems or processes. Signup and view all the answers

What is a data mart?

A departmental small-scale 'Data Warehouse' that stores only limited/relevant data. Signup and view all the answers

What is a dependent data mart?

A subset that is created directly from a data warehouse. Signup and view all the answers

What is an independent data mart?

A small data warehouse designed for a strategic business unit or a department. Signup and view all the answers

What are Operational Data Stores (ODS)?

A database often used as an interim area for a data warehouse. Signup and view all the answers

What is an Enterprise Data Warehouse (EDW)?

A data warehouse for the entire enterprise. Signup and view all the answers

What is Meta Data?

Data about data. It describes the contents of a data warehouse and the manner of its acquisition and use. Signup and view all the answers

What is the Data Warehouse Framework?

Data Sources -> ETL Process -> Enterprise Data Warehouse -> Data marts -> Applications and visualization. Signup and view all the answers

What are types of data sources that feed a DW?

ERP, Legacy, POS, Other OLTP/web, External Data. Signup and view all the answers

What does ETL stand for?

Extract, Transform, Load. Signup and view all the answers

What is data integration?

Integration that comprises three major processes: Data access, data federation and change capture. Signup and view all the answers

What is EAI?

Enterprise Application Integration: A technology that provides a vehicle for pushing data from source systems into a data warehouse. Signup and view all the answers

What is EII?

Enterprise Information Integration: An evolving tool space that promises real-time integration from a variety of sources. Signup and view all the answers

What is OLTP?

OnLine Transaction Processing. Live data, online transaction processing. Signup and view all the answers

What is OLAP?

OnLine Analytical Processing. Converts data to information for decision support. Signup and view all the answers

What factors can cause failure in data warehouses?

Lack of executive sponsorship, unclear business objectives, cultural issues being ignored, change management, unrealistic expectations, inappropriate architecture, low data quality / missing information, loading data just because it is available. Signup and view all the answers

What is the definition of good scalability?

Good scalability means that queries and other data-access functions will grow linearly with the size of the warehouse. Signup and view all the answers

What is Actionable Insight?

Information that can be acted upon or gives insights into future actions. Signup and view all the answers

What are the disciplines involved in data mining?

Statistics, AI, MIS / Management Science Systems. Signup and view all the answers

What is the definition of data?

A collection of facts usually obtained as the result of experiences, observations, or experiments. Signup and view all the answers

What is categorical data?

Yes or no. True or false, etc. Signup and view all the answers

What is numerical data?

1, 2, 3, 4. Signup and view all the answers

What is unstructured data?

Tweets, Facebook posts, etc. Signup and view all the answers

What are some data mining applications?

Customer Relationship Management, Banking & Financial Decisions, Retail & Logistics, Manufacturing & Maintenance. Signup and view all the answers

What sectors use data mining applications?

Computer hardware and software; science and engineering; government and defense; homeland security and law enforcement; travel industry; entertainment industry; sports; medicine & healthcare. Signup and view all the answers

What are the three Data Mining Processes?

CRISP-DM, SEMMA, KDD. Signup and view all the answers

What are the steps of CRISP-DM?

Business Understanding; Data understanding; Data preparation; Model building; Testing and evaluations; Deployment. Signup and view all the answers

Which steps in the CRISP-DM process take the longest?

Business understanding; data understanding; data preparation. Signup and view all the answers

What is business intelligence?

BI is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. Signup and view all the answers

What are the four components of BI?

A data warehouse, business analytics, business performance management, a user interface. Signup and view all the answers

What are Thompson's benefits of BI?

Faster, more accurate reporting; Improved decision making; Improved customer service; Increased revenue. Signup and view all the answers

What does management weigh-in mean to a data project?

You MUST have a high-up person who believes in the project or it'll die. Signup and view all the answers

What are the 4 steps of a data presentation?

State the business objective, use the data, make observations, make recommendations. Signup and view all the answers

What's the primary goal of a data scientist?

Support to better facilitate the processes of the business. Signup and view all the answers

What is a data lake?

A large pool of data that isn't formatted yet. Signup and view all the answers

What causes inaccuracies in business intelligence?

Single sources of data, incompatible data, incomplete data. Signup and view all the answers

What is a subset of data?

A sample of data. Signup and view all the answers

Study Notes

Data Warehousing Concepts

A Data Warehouse serves as a single version of truth for an organization, centralizing and cleansing data for enterprise-wide access.
Defined as a physical repository that organizes relational data to provide standardized and relevant information across the enterprise.
The data warehouse is subject-oriented, integrated, and designed to support Decision Support System (DSS) functions, ensuring data is non-volatile and tied to a specific timeframe.

Types of Data Warehouses

Data Mart: A smaller-scale data warehouse focused on a specific department, containing limited but relevant data.
Dependent Data Mart: Created directly from a data warehouse, relying on its data.
Independent Data Mart: A standalone data warehouse for a specific business unit, operating independently from the central warehouse.
Operational Data Store (ODS): Acts as an interim area for data before it is loaded into the data warehouse.
Enterprise Data Warehouse (EDW): A comprehensive data warehouse that serves the entire organization.

Data Management Processes

ETL Process: Involves Extracting data from various sources, Transforming it into a useful format, and Loading it into a warehouse. Key for integrating data effectively.
Data Integration: Encompasses accessing, federating, and capturing changes in data from various sources.
Enterprise Application Integration (EAI): Facilitates data transfer from source systems to a data warehouse.
Enterprise Information Integration (EII): Emerges as a tool for real-time integration from diverse data sources.

Data Types and Analysis

Relational Data: Organized data that allows different systems to interoperate, such as linking different codes across systems.
Categorical Data: Consists of binary or nominal responses.
Numerical Data: Comprises quantitative values.
Unstructured Data: Includes free-format data like social media posts and messages.
Data Mining Applications: Utilized in sectors like CRM, banking, retail, and healthcare to derive insights.

Data Mining and Business Intelligence

Data Mining Processes: CRISP-DM, SEMMA, and KDD serve as standardized methodologies for data analysis.
Steps in CRISP-DM include Business Understanding, Data Preparation, Model Building, and Evaluation—phases one, two, and three often consume the most time.
Business Intelligence (BI): Integrates various architectures, tools, and applications to enable data analysis, transforming data into actionable insights for decision-makers.

Success Factors and Challenges

Key factors impacting data warehouse architecture include resource constraints, strategic alignment, and data quality.
Non-technical factors for project success include executive sponsorship, clear business objectives, and effective change management.
Scalability should allow data access functions to function efficiently as the volume of data increases.

Key Insights and Relationships

Actionable Insights enable data-driven decisions, bridging the gap between raw data and strategic actions.
Team collaborations across disciplines such as statistics, AI, and management science enhance the effectiveness of data mining practices.
Inaccuracies in BI stem from single sources, incompatible data, and incomplete datasets impacting decision quality.

Presentation and Communication

Effective data presentation includes stating objectives, crunching relevant data, making observations, and offering clear recommendations without jargon.
Management buy-in is crucial for data project success; without belief from high-level stakeholders, projects risk failure.

The Role of Data Scientists

The main goal of data scientists is to facilitate and enhance business processes through data analysis and interpretation.

Challenges in BI

Real-time data is increasingly sought after for immediate insights, although it can be costly to implement.
Data Lakes represent pools of unstructured data serving as storage before formal organization, contrasting with structured data warehouses which provide not just storage but also analysis capabilities.

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Description

Prepare for your Business Intelligence Exam with these flashcards focusing on key concepts such as data warehouses and their importance. This review covers essential definitions and functions crucial for understanding data management in a business context.